Targeting of cell death and neuroinflammation with peptide-linked iron oxide nanoparticles and Gd-DTPA in a mouse model of Parkinson's disease

Coralie Sclavons, Sébastien Boutry, Sophie Laurent, Luce Vander Elst, Robert N Muller


Parkinson’s disease (PD) is one of the most common neurodegenerative disease and remains difficult to diagnose by conventional methods of early detection. It is characterized by the apoptotic loss of dopaminergic neurons (DN) and a neuroinflammation mainly located in the ventral midbrain (VM).

The aim of this work is to study new vectorized contrast agents for magnetic resonance imaging (MRI) detection of PD injured areas by targeting apoptosis and inflammation. Two peptides selected by phage display were used for the experiments: R826 peptide, selected for its affinity for the phosphatidylserine (PS) exposed at the external surface of apoptotic cells, and 2C peptide, selected for its affinity for TNF-α (tumor necrosis factor alpha), one of the most abundant cytokines secreted during inflammation. These peptides were grafted to pegylated iron oxide nanoparticles (PEG-USPIO) and gadolinium-diethylenetriamine pentaacetic acid (Gd-DTPA) for in vitro and in vivo studies, respectively. PD was simulated on mice with the MPTP (1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine) neurotoxin and its active metabolite MPP+ (1-methyl-4-phenylpyridinium) for in vitro studies.

The results showed that PEG-USPIO-R826 and PEG-USPIO-2C enabled the detection of apoptosis and inflammation, respectively, on MPP+-treated culture cells. PEG-USPIO-2C also allowed detection of inflammation on histological brain sections of MPTP-treated mice. The 2C peptide grafted to Gd-DTPA showed encouraging results in MRI detection of injured brain areas in MPTP-treated mice. These observations suggest that a targeting of damaged cells and injured areas by these new specific contrast agents occurs, offering a new tool for early diagnosis of neurodegenerative disorders like PD by MRI.


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Journal of Biomedical Engineering and Informatics

ISSN 2377-9381(Print)  ISSN 2377-939X(Online)

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